GitHub

[OTHER]：Add Federated Learning (FL) - Horizontal Federated Logistic Regression Implementation

I would like to contribute a lightweight and optimized implementation of Horizontal Federated Logistic Regression (2025 optimized version) to this project. This implementation is tailored for ...
IEEE

Implementation of Logistic Regression for Classification of Chronic Kidney Disease with and without Comorbidities: A Performance Evaluation

Abstract: Chronic kidney disease (CKD) is a highly progressive condition that can lead to kidney failure and requires early detection to prevent serious complications. This study analyzes the ...
Frontiers

Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models

Objective: To develop and validate a machine learning (ML)-based prediction model of Bethesda III nodules and create a nomogram based on the best model. Methods: We collected data on patients with ...
IEEE

Fraud Detection Using Machine Learning: An Evaluation of Logistic Regression and Random Forest

Abstract: With the increasing importance of digital security in the current world of finance, it is a must to find ways to implement artificial intelligence techniques to detect financial fraud ...
Frontiers

Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa

Background: Maternal and child health remains a global public health issue, particularly in low- and middle-income countries where maternal and child mortality are extremely high. The World Health ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
BMJ

Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey

Objectives This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data. Design ...